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  1. Child Mind Institute - Problematic Internet Usage Kaggle Competition
    1. Introduction - Problem Definition
    2. Data Preparation
    3. Data Exploration
    4. Data Pre-Processing
    5. Modeling
      1. Hyperparameter Tuning
      2. Hyperparameter Tuning with GridSearchCV
      3. Generating predictions for submission
    6. Results & Conclusion
      1. Results:
      2. Limitations:
      3. Next Steps:
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# Child Mind Institute - Problematic Internet Usage Kaggle Competition

Child Mind Institute - Problematic Internet Usage Kaggle Competition¶

## Introduction - Problem Definition

Introduction - Problem Definition¶

The Child Mind Institute has collected data on the physical activity habits of children and adolescents from various means including surveys, physical fitness tests, and actigraphy (a research-grade biotracker of physical activity). Can this data be used to predict the level of problematic internet usage that an individual may display?

The Child Mind Institute has collected data on the physical activity habits of children and adolescents from various means including surveys, physical fitness tests, and actigraphy (a research-grade biotracker of physical activity). Can this data be used to predict the level of problematic internet usage that an individual may display?

## Data Preparation

Data Preparation¶

Connecting to the competition datasets on Kaggle and importing necessary libraries/packages:

Connecting to the competition datasets on Kaggle and importing necessary libraries/packages:

[43]:
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import cohen_kappa_score
from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier, AdaBoostClassifier, BaggingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import make_scorer

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))


/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv
/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv
/kaggle/input/child-mind-institute-problematic-internet-use/train.csv
/kaggle/input/child-mind-institute-problematic-internet-use/test.csv
/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet/id=00115b9f/part-0.parquet
/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet/id=001f3379/part-0.parquet
/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/id=0745c390/part-0.parquet
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[44]:
from concurrent.futures import ThreadPoolExecutor
import os
from tqdm import tqdm
def process_file(filename, dirname):
df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))
df.drop('step', axis=1, inplace=True)
return df.describe().values.reshape(-1), filename.split('=')[1]

def load_time_series(dirname) -> pd.DataFrame:
ids = os.listdir(dirname)
with ThreadPoolExecutor() as executor:
results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))
stats, indexes = zip(*results)
df = pd.DataFrame(stats, columns=[f"Stat_{i}" for i in range(len(stats[0]))])
df['id'] = indexes
return df

train_ts = load_time_series("/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet")
test_ts = load_time_series("/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet")
time_series_cols = train_ts.columns.tolist()
time_series_cols.remove("id")
100%|██████████| 996/996 [01:28<00:00, 11.23it/s]
100%|██████████| 2/2 [00:00<00:00, 10.09it/s]
## Data Exploration

Data Exploration¶

Viewing the contents of the various datasets:

Viewing the contents of the various datasets:

[45]:
train_ds =pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')
train_ds.head()
[45]:
id Basic_Demos-Enroll_Season Basic_Demos-Age Basic_Demos-Sex CGAS-Season CGAS-CGAS_Score Physical-Season Physical-BMI Physical-Height Physical-Weight ... PCIAT-PCIAT_18 PCIAT-PCIAT_19 PCIAT-PCIAT_20 PCIAT-PCIAT_Total SDS-Season SDS-SDS_Total_Raw SDS-SDS_Total_T PreInt_EduHx-Season PreInt_EduHx-computerinternet_hoursday sii
0 00008ff9 Fall 5 0 Winter 51.0 Fall 16.877316 46.0 50.8 ... 4.0 2.0 4.0 55.0 NaN NaN NaN Fall 3.0 2.0
1 000fd460 Summer 9 0 NaN NaN Fall 14.035590 48.0 46.0 ... 0.0 0.0 0.0 0.0 Fall 46.0 64.0 Summer 0.0 0.0
2 00105258 Summer 10 1 Fall 71.0 Fall 16.648696 56.5 75.6 ... 2.0 1.0 1.0 28.0 Fall 38.0 54.0 Summer 2.0 0.0
3 00115b9f Winter 9 0 Fall 71.0 Summer 18.292347 56.0 81.6 ... 3.0 4.0 1.0 44.0 Summer 31.0 45.0 Winter 0.0 1.0
4 0016bb22 Spring 18 1 Summer NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 82 columns

[46]:
test_ds =pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')
test_ds.head()
[46]:
id Basic_Demos-Enroll_Season Basic_Demos-Age Basic_Demos-Sex CGAS-Season CGAS-CGAS_Score Physical-Season Physical-BMI Physical-Height Physical-Weight ... BIA-BIA_TBW PAQ_A-Season PAQ_A-PAQ_A_Total PAQ_C-Season PAQ_C-PAQ_C_Total SDS-Season SDS-SDS_Total_Raw SDS-SDS_Total_T PreInt_EduHx-Season PreInt_EduHx-computerinternet_hoursday
0 00008ff9 Fall 5 0 Winter 51.0 Fall 16.877316 46.0 50.8 ... 32.6909 NaN NaN NaN NaN NaN NaN NaN Fall 3.0
1 000fd460 Summer 9 0 NaN NaN Fall 14.035590 48.0 46.0 ... 27.0552 NaN NaN Fall 2.340 Fall 46.0 64.0 Summer 0.0
2 00105258 Summer 10 1 Fall 71.0 Fall 16.648696 56.5 75.6 ... NaN NaN NaN Summer 2.170 Fall 38.0 54.0 Summer 2.0
3 00115b9f Winter 9 0 Fall 71.0 Summer 18.292347 56.0 81.6 ... 45.9966 NaN NaN Winter 2.451 Summer 31.0 45.0 Winter 0.0
4 0016bb22 Spring 18 1 Summer NaN NaN NaN NaN NaN ... NaN Summer 1.04 NaN NaN NaN NaN NaN NaN NaN

5 rows × 59 columns

[47]:
data_dict = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv')
data_dict.head()
[47]:
Instrument Field Description Type Values Value Labels
0 Identifier id Participant's ID str NaN NaN
1 Demographics Basic_Demos-Enroll_Season Season of enrollment str Spring, Summer, Fall, Winter NaN
2 Demographics Basic_Demos-Age Age of participant float NaN NaN
3 Demographics Basic_Demos-Sex Sex of participant categorical int 0,1 0=Male, 1=Female
4 Children's Global Assessment Scale CGAS-Season Season of participation str Spring, Summer, Fall, Winter NaN
[48]:
train_ts.head()
[48]:
Stat_0 Stat_1 Stat_2 Stat_3 Stat_4 Stat_5 Stat_6 Stat_7 Stat_8 Stat_9 ... Stat_87 Stat_88 Stat_89 Stat_90 Stat_91 Stat_92 Stat_93 Stat_94 Stat_95 id
0 50458.0 50458.0 50458.0 50458.0 50458.0 50458.0 50458.0 50458.0 50458.0 50458.0 ... 5.314874 89.422226 0.0 2626.199951 4187.0 8.639500e+13 7.0 2.0 57.0 0745c390
1 340584.0 340584.0 340584.0 340584.0 340584.0 340584.0 340584.0 340584.0 340584.0 340584.0 ... 3.966906 89.080330 1.0 2628.199951 4146.0 8.639500e+13 7.0 2.0 243.0 eaab7a96
2 40003.0 40003.0 40003.0 40003.0 40003.0 40003.0 40003.0 40003.0 40003.0 40003.0 ... 5.066334 86.987267 0.0 2618.199951 4183.0 8.636500e+13 7.0 3.0 134.0 8ec2cc63
3 223915.0 223915.0 223915.0 223915.0 223915.0 223915.0 223915.0 223915.0 223915.0 223915.0 ... 6.134459 89.976074 0.0 2502.000000 6000.0 8.639500e+13 7.0 4.0 72.0 b2987a65
4 15420.0 15420.0 15420.0 15420.0 15420.0 15420.0 15420.0 15420.0 15420.0 15420.0 ... 2.774382 89.300034 0.0 1046.800049 4199.0 8.601500e+13 7.0 4.0 76.0 7b8842c3

5 rows × 97 columns

[49]:
train_ts.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 996 entries, 0 to 995
Data columns (total 97 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Stat_0   996 non-null    float64
 1   Stat_1   996 non-null    float64
 2   Stat_2   996 non-null    float64
 3   Stat_3   996 non-null    float64
 4   Stat_4   996 non-null    float64
 5   Stat_5   996 non-null    float64
 6   Stat_6   996 non-null    float64
 7   Stat_7   996 non-null    float64
 8   Stat_8   996 non-null    float64
 9   Stat_9   996 non-null    float64
 10  Stat_10  996 non-null    float64
 11  Stat_11  996 non-null    float64
 12  Stat_12  996 non-null    float64
 13  Stat_13  996 non-null    float64
 14  Stat_14  996 non-null    float64
 15  Stat_15  996 non-null    float64
 16  Stat_16  996 non-null    float64
 17  Stat_17  996 non-null    float64
 18  Stat_18  996 non-null    float64
 19  Stat_19  996 non-null    float64
 20  Stat_20  996 non-null    float64
 21  Stat_21  996 non-null    float64
 22  Stat_22  996 non-null    float64
 23  Stat_23  996 non-null    float64
 24  Stat_24  996 non-null    float64
 25  Stat_25  996 non-null    float64
 26  Stat_26  996 non-null    float64
 27  Stat_27  996 non-null    float64
 28  Stat_28  996 non-null    float64
 29  Stat_29  996 non-null    float64
 30  Stat_30  996 non-null    float64
 31  Stat_31  996 non-null    float64
 32  Stat_32  996 non-null    float64
 33  Stat_33  996 non-null    float64
 34  Stat_34  996 non-null    float64
 35  Stat_35  996 non-null    float64
 36  Stat_36  996 non-null    float64
 37  Stat_37  996 non-null    float64
 38  Stat_38  996 non-null    float64
 39  Stat_39  996 non-null    float64
 40  Stat_40  996 non-null    float64
 41  Stat_41  996 non-null    float64
 42  Stat_42  996 non-null    float64
 43  Stat_43  996 non-null    float64
 44  Stat_44  996 non-null    float64
 45  Stat_45  996 non-null    float64
 46  Stat_46  996 non-null    float64
 47  Stat_47  996 non-null    float64
 48  Stat_48  996 non-null    float64
 49  Stat_49  996 non-null    float64
 50  Stat_50  996 non-null    float64
 51  Stat_51  996 non-null    float64
 52  Stat_52  996 non-null    float64
 53  Stat_53  996 non-null    float64
 54  Stat_54  996 non-null    float64
 55  Stat_55  996 non-null    float64
 56  Stat_56  996 non-null    float64
 57  Stat_57  996 non-null    float64
 58  Stat_58  996 non-null    float64
 59  Stat_59  996 non-null    float64
 60  Stat_60  996 non-null    float64
 61  Stat_61  996 non-null    float64
 62  Stat_62  996 non-null    float64
 63  Stat_63  996 non-null    float64
 64  Stat_64  996 non-null    float64
 65  Stat_65  996 non-null    float64
 66  Stat_66  996 non-null    float64
 67  Stat_67  996 non-null    float64
 68  Stat_68  996 non-null    float64
 69  Stat_69  996 non-null    float64
 70  Stat_70  996 non-null    float64
 71  Stat_71  996 non-null    float64
 72  Stat_72  996 non-null    float64
 73  Stat_73  996 non-null    float64
 74  Stat_74  996 non-null    float64
 75  Stat_75  996 non-null    float64
 76  Stat_76  996 non-null    float64
 77  Stat_77  996 non-null    float64
 78  Stat_78  996 non-null    float64
 79  Stat_79  996 non-null    float64
 80  Stat_80  996 non-null    float64
 81  Stat_81  996 non-null    float64
 82  Stat_82  996 non-null    float64
 83  Stat_83  996 non-null    float64
 84  Stat_84  996 non-null    float64
 85  Stat_85  996 non-null    float64
 86  Stat_86  996 non-null    float64
 87  Stat_87  996 non-null    float64
 88  Stat_88  996 non-null    float64
 89  Stat_89  996 non-null    float64
 90  Stat_90  996 non-null    float64
 91  Stat_91  996 non-null    float64
 92  Stat_92  996 non-null    float64
 93  Stat_93  996 non-null    float64
 94  Stat_94  996 non-null    float64
 95  Stat_95  996 non-null    float64
 96  id       996 non-null    object 
dtypes: float64(96), object(1)
memory usage: 754.9+ KB
[50]:
test_ts.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 97 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Stat_0   2 non-null      float64
 1   Stat_1   2 non-null      float64
 2   Stat_2   2 non-null      float64
 3   Stat_3   2 non-null      float64
 4   Stat_4   2 non-null      float64
 5   Stat_5   2 non-null      float64
 6   Stat_6   2 non-null      float64
 7   Stat_7   2 non-null      float64
 8   Stat_8   2 non-null      float64
 9   Stat_9   2 non-null      float64
 10  Stat_10  2 non-null      float64
 11  Stat_11  2 non-null      float64
 12  Stat_12  2 non-null      float64
 13  Stat_13  2 non-null      float64
 14  Stat_14  2 non-null      float64
 15  Stat_15  2 non-null      float64
 16  Stat_16  2 non-null      float64
 17  Stat_17  2 non-null      float64
 18  Stat_18  2 non-null      float64
 19  Stat_19  2 non-null      float64
 20  Stat_20  2 non-null      float64
 21  Stat_21  2 non-null      float64
 22  Stat_22  2 non-null      float64
 23  Stat_23  2 non-null      float64
 24  Stat_24  2 non-null      float64
 25  Stat_25  2 non-null      float64
 26  Stat_26  2 non-null      float64
 27  Stat_27  2 non-null      float64
 28  Stat_28  2 non-null      float64
 29  Stat_29  2 non-null      float64
 30  Stat_30  2 non-null      float64
 31  Stat_31  2 non-null      float64
 32  Stat_32  2 non-null      float64
 33  Stat_33  2 non-null      float64
 34  Stat_34  2 non-null      float64
 35  Stat_35  2 non-null      float64
 36  Stat_36  2 non-null      float64
 37  Stat_37  2 non-null      float64
 38  Stat_38  2 non-null      float64
 39  Stat_39  2 non-null      float64
 40  Stat_40  2 non-null      float64
 41  Stat_41  2 non-null      float64
 42  Stat_42  2 non-null      float64
 43  Stat_43  2 non-null      float64
 44  Stat_44  2 non-null      float64
 45  Stat_45  2 non-null      float64
 46  Stat_46  2 non-null      float64
 47  Stat_47  2 non-null      float64
 48  Stat_48  2 non-null      float64
 49  Stat_49  2 non-null      float64
 50  Stat_50  2 non-null      float64
 51  Stat_51  2 non-null      float64
 52  Stat_52  2 non-null      float64
 53  Stat_53  2 non-null      float64
 54  Stat_54  2 non-null      float64
 55  Stat_55  2 non-null      float64
 56  Stat_56  2 non-null      float64
 57  Stat_57  2 non-null      float64
 58  Stat_58  2 non-null      float64
 59  Stat_59  2 non-null      float64
 60  Stat_60  2 non-null      float64
 61  Stat_61  2 non-null      float64
 62  Stat_62  2 non-null      float64
 63  Stat_63  2 non-null      float64
 64  Stat_64  2 non-null      float64
 65  Stat_65  2 non-null      float64
 66  Stat_66  2 non-null      float64
 67  Stat_67  2 non-null      float64
 68  Stat_68  2 non-null      float64
 69  Stat_69  2 non-null      float64
 70  Stat_70  2 non-null      float64
 71  Stat_71  2 non-null      float64
 72  Stat_72  2 non-null      float64
 73  Stat_73  2 non-null      float64
 74  Stat_74  2 non-null      float64
 75  Stat_75  2 non-null      float64
 76  Stat_76  2 non-null      float64
 77  Stat_77  2 non-null      float64
 78  Stat_78  2 non-null      float64
 79  Stat_79  2 non-null      float64
 80  Stat_80  2 non-null      float64
 81  Stat_81  2 non-null      float64
 82  Stat_82  2 non-null      float64
 83  Stat_83  2 non-null      float64
 84  Stat_84  2 non-null      float64
 85  Stat_85  2 non-null      float64
 86  Stat_86  2 non-null      float64
 87  Stat_87  2 non-null      float64
 88  Stat_88  2 non-null      float64
 89  Stat_89  2 non-null      float64
 90  Stat_90  2 non-null      float64
 91  Stat_91  2 non-null      float64
 92  Stat_92  2 non-null      float64
 93  Stat_93  2 non-null      float64
 94  Stat_94  2 non-null      float64
 95  Stat_95  2 non-null      float64
 96  id       2 non-null      object 
dtypes: float64(96), object(1)
memory usage: 1.6+ KB
[51]:
train_ds.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3960 entries, 0 to 3959
Data columns (total 82 columns):
 #   Column                                  Non-Null Count  Dtype  
---  ------                                  --------------  -----  
 0   id                                      3960 non-null   object 
 1   Basic_Demos-Enroll_Season               3960 non-null   object 
 2   Basic_Demos-Age                         3960 non-null   int64  
 3   Basic_Demos-Sex                         3960 non-null   int64  
 4   CGAS-Season                             2555 non-null   object 
 5   CGAS-CGAS_Score                         2421 non-null   float64
 6   Physical-Season                         3310 non-null   object 
 7   Physical-BMI                            3022 non-null   float64
 8   Physical-Height                         3027 non-null   float64
 9   Physical-Weight                         3076 non-null   float64
 10  Physical-Waist_Circumference            898 non-null    float64
 11  Physical-Diastolic_BP                   2954 non-null   float64
 12  Physical-HeartRate                      2967 non-null   float64
 13  Physical-Systolic_BP                    2954 non-null   float64
 14  Fitness_Endurance-Season                1308 non-null   object 
 15  Fitness_Endurance-Max_Stage             743 non-null    float64
 16  Fitness_Endurance-Time_Mins             740 non-null    float64
 17  Fitness_Endurance-Time_Sec              740 non-null    float64
 18  FGC-Season                              3346 non-null   object 
 19  FGC-FGC_CU                              2322 non-null   float64
 20  FGC-FGC_CU_Zone                         2282 non-null   float64
 21  FGC-FGC_GSND                            1074 non-null   float64
 22  FGC-FGC_GSND_Zone                       1062 non-null   float64
 23  FGC-FGC_GSD                             1074 non-null   float64
 24  FGC-FGC_GSD_Zone                        1063 non-null   float64
 25  FGC-FGC_PU                              2310 non-null   float64
 26  FGC-FGC_PU_Zone                         2271 non-null   float64
 27  FGC-FGC_SRL                             2305 non-null   float64
 28  FGC-FGC_SRL_Zone                        2267 non-null   float64
 29  FGC-FGC_SRR                             2307 non-null   float64
 30  FGC-FGC_SRR_Zone                        2269 non-null   float64
 31  FGC-FGC_TL                              2324 non-null   float64
 32  FGC-FGC_TL_Zone                         2285 non-null   float64
 33  BIA-Season                              2145 non-null   object 
 34  BIA-BIA_Activity_Level_num              1991 non-null   float64
 35  BIA-BIA_BMC                             1991 non-null   float64
 36  BIA-BIA_BMI                             1991 non-null   float64
 37  BIA-BIA_BMR                             1991 non-null   float64
 38  BIA-BIA_DEE                             1991 non-null   float64
 39  BIA-BIA_ECW                             1991 non-null   float64
 40  BIA-BIA_FFM                             1991 non-null   float64
 41  BIA-BIA_FFMI                            1991 non-null   float64
 42  BIA-BIA_FMI                             1991 non-null   float64
 43  BIA-BIA_Fat                             1991 non-null   float64
 44  BIA-BIA_Frame_num                       1991 non-null   float64
 45  BIA-BIA_ICW                             1991 non-null   float64
 46  BIA-BIA_LDM                             1991 non-null   float64
 47  BIA-BIA_LST                             1991 non-null   float64
 48  BIA-BIA_SMM                             1991 non-null   float64
 49  BIA-BIA_TBW                             1991 non-null   float64
 50  PAQ_A-Season                            475 non-null    object 
 51  PAQ_A-PAQ_A_Total                       475 non-null    float64
 52  PAQ_C-Season                            1721 non-null   object 
 53  PAQ_C-PAQ_C_Total                       1721 non-null   float64
 54  PCIAT-Season                            2736 non-null   object 
 55  PCIAT-PCIAT_01                          2733 non-null   float64
 56  PCIAT-PCIAT_02                          2734 non-null   float64
 57  PCIAT-PCIAT_03                          2731 non-null   float64
 58  PCIAT-PCIAT_04                          2731 non-null   float64
 59  PCIAT-PCIAT_05                          2729 non-null   float64
 60  PCIAT-PCIAT_06                          2732 non-null   float64
 61  PCIAT-PCIAT_07                          2729 non-null   float64
 62  PCIAT-PCIAT_08                          2730 non-null   float64
 63  PCIAT-PCIAT_09                          2730 non-null   float64
 64  PCIAT-PCIAT_10                          2733 non-null   float64
 65  PCIAT-PCIAT_11                          2734 non-null   float64
 66  PCIAT-PCIAT_12                          2731 non-null   float64
 67  PCIAT-PCIAT_13                          2729 non-null   float64
 68  PCIAT-PCIAT_14                          2732 non-null   float64
 69  PCIAT-PCIAT_15                          2730 non-null   float64
 70  PCIAT-PCIAT_16                          2728 non-null   float64
 71  PCIAT-PCIAT_17                          2725 non-null   float64
 72  PCIAT-PCIAT_18                          2728 non-null   float64
 73  PCIAT-PCIAT_19                          2730 non-null   float64
 74  PCIAT-PCIAT_20                          2733 non-null   float64
 75  PCIAT-PCIAT_Total                       2736 non-null   float64
 76  SDS-Season                              2618 non-null   object 
 77  SDS-SDS_Total_Raw                       2609 non-null   float64
 78  SDS-SDS_Total_T                         2606 non-null   float64
 79  PreInt_EduHx-Season                     3540 non-null   object 
 80  PreInt_EduHx-computerinternet_hoursday  3301 non-null   float64
 81  sii                                     2736 non-null   float64
dtypes: float64(68), int64(2), object(12)
memory usage: 2.5+ MB
[52]:
test_ds.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20 entries, 0 to 19
Data columns (total 59 columns):
 #   Column                                  Non-Null Count  Dtype  
---  ------                                  --------------  -----  
 0   id                                      20 non-null     object 
 1   Basic_Demos-Enroll_Season               20 non-null     object 
 2   Basic_Demos-Age                         20 non-null     int64  
 3   Basic_Demos-Sex                         20 non-null     int64  
 4   CGAS-Season                             10 non-null     object 
 5   CGAS-CGAS_Score                         8 non-null      float64
 6   Physical-Season                         14 non-null     object 
 7   Physical-BMI                            13 non-null     float64
 8   Physical-Height                         13 non-null     float64
 9   Physical-Weight                         13 non-null     float64
 10  Physical-Waist_Circumference            5 non-null      float64
 11  Physical-Diastolic_BP                   11 non-null     float64
 12  Physical-HeartRate                      12 non-null     float64
 13  Physical-Systolic_BP                    11 non-null     float64
 14  Fitness_Endurance-Season                4 non-null      object 
 15  Fitness_Endurance-Max_Stage             3 non-null      float64
 16  Fitness_Endurance-Time_Mins             3 non-null      float64
 17  Fitness_Endurance-Time_Sec              3 non-null      float64
 18  FGC-Season                              17 non-null     object 
 19  FGC-FGC_CU                              13 non-null     float64
 20  FGC-FGC_CU_Zone                         13 non-null     float64
 21  FGC-FGC_GSND                            5 non-null      float64
 22  FGC-FGC_GSND_Zone                       5 non-null      float64
 23  FGC-FGC_GSD                             5 non-null      float64
 24  FGC-FGC_GSD_Zone                        5 non-null      float64
 25  FGC-FGC_PU                              13 non-null     float64
 26  FGC-FGC_PU_Zone                         13 non-null     float64
 27  FGC-FGC_SRL                             13 non-null     float64
 28  FGC-FGC_SRL_Zone                        13 non-null     float64
 29  FGC-FGC_SRR                             13 non-null     float64
 30  FGC-FGC_SRR_Zone                        13 non-null     float64
 31  FGC-FGC_TL                              13 non-null     float64
 32  FGC-FGC_TL_Zone                         13 non-null     float64
 33  BIA-Season                              8 non-null      object 
 34  BIA-BIA_Activity_Level_num              8 non-null      float64
 35  BIA-BIA_BMC                             8 non-null      float64
 36  BIA-BIA_BMI                             8 non-null      float64
 37  BIA-BIA_BMR                             8 non-null      float64
 38  BIA-BIA_DEE                             8 non-null      float64
 39  BIA-BIA_ECW                             8 non-null      float64
 40  BIA-BIA_FFM                             8 non-null      float64
 41  BIA-BIA_FFMI                            8 non-null      float64
 42  BIA-BIA_FMI                             8 non-null      float64
 43  BIA-BIA_Fat                             8 non-null      float64
 44  BIA-BIA_Frame_num                       8 non-null      float64
 45  BIA-BIA_ICW                             8 non-null      float64
 46  BIA-BIA_LDM                             8 non-null      float64
 47  BIA-BIA_LST                             8 non-null      float64
 48  BIA-BIA_SMM                             8 non-null      float64
 49  BIA-BIA_TBW                             8 non-null      float64
 50  PAQ_A-Season                            1 non-null      object 
 51  PAQ_A-PAQ_A_Total                       1 non-null      float64
 52  PAQ_C-Season                            9 non-null      object 
 53  PAQ_C-PAQ_C_Total                       9 non-null      float64
 54  SDS-Season                              10 non-null     object 
 55  SDS-SDS_Total_Raw                       10 non-null     float64
 56  SDS-SDS_Total_T                         10 non-null     float64
 57  PreInt_EduHx-Season                     18 non-null     object 
 58  PreInt_EduHx-computerinternet_hoursday  16 non-null     float64
dtypes: float64(46), int64(2), object(11)
memory usage: 9.3+ KB
[53]:
#find intersection of IDs in train_ds and train_ts (which IDs are contained in both datasets)

train_ids = train_ds['id'].to_list()
parq_ids = train_ts['id'].to_list()

intersection_ids = (set(train_ids)).intersection(set(parq_ids))
print(len(intersection_ids))
996
[54]:
data_dict.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 81 entries, 0 to 80
Data columns (total 6 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Instrument    81 non-null     object
 1   Field         81 non-null     object
 2   Description   81 non-null     object
 3   Type          81 non-null     object
 4   Values        42 non-null     object
 5   Value Labels  32 non-null     object
dtypes: object(6)
memory usage: 3.9+ KB
[55]:
data_dict['Instrument'].unique()
[55]:
array(['Identifier', 'Demographics', "Children's Global Assessment Scale",
       'Physical Measures', 'FitnessGram Vitals and Treadmill',
       'FitnessGram Child', 'Bio-electric Impedance Analysis',
       'Physical Activity Questionnaire (Adolescents)',
       'Physical Activity Questionnaire (Children)',
       'Parent-Child Internet Addiction Test', 'Sleep Disturbance Scale',
       'Internet Use'], dtype=object)
[56]:
import seaborn as sns
## Data Pre-Processing

Data Pre-Processing¶

##### Feature(s): Physical_BMI, BIA-BIA_BMI
Feature(s): Physical_BMI, BIA-BIA_BMI¶
There are two columns containing BMI values from different measures. I want to combine these into one column, taking the mean if both BMIs are not null, or only taking the non-null BMI value. If both BMI values are null, the combined BMI column will remain null.

There are two columns containing BMI values from different measures. I want to combine these into one column, taking the mean if both BMIs are not null, or only taking the non-null BMI value. If both BMI values are null, the combined BMI column will remain null.

[57]:
train_ds['Physical-BMI'].isna().sum()
[57]:
938
[58]:
train_ds['BIA-BIA_BMI'].isna().sum()
[58]:
1969
Since both BMI features contain a decent amount of null values, I will combine them into one feature. If only one of the two is null, I will take the non-null value, if neither is null I will take the mean, and if both are null, the value will remain null.

Since both BMI features contain a decent amount of null values, I will combine them into one feature. If only one of the two is null, I will take the non-null value, if neither is null I will take the mean, and if both are null, the value will remain null.

[59]:
list_cols = ['BIA-BIA_BMI', 'Physical-BMI']
train_ds['BMI_combined'] = train_ds.loc[:,list_cols].sum(axis=1, skipna=False)
test_ds['BMI_combined'] = test_ds.loc[:,list_cols].sum(axis=1, skipna=False)

[60]:
train_ds['BMI_combined'] = train_ds['BMI_combined']/2
test_ds['BMI_combined'] = test_ds['BMI_combined']/2
[61]:
#if only one of the two BMI columns is null, filling in BMI_combined values with the other value
train_ds['BMI_combined'] = train_ds['BMI_combined'].fillna(train_ds['Physical-BMI'])
train_ds['BMI_combined'] = train_ds['BMI_combined'].fillna(train_ds['BIA-BIA_BMI'])

test_ds['BMI_combined'] = test_ds['BMI_combined'].fillna(test_ds['Physical-BMI'])
test_ds['BMI_combined'] = test_ds['BMI_combined'].fillna(test_ds['BIA-BIA_BMI'])
[62]:
# checking that this worked as intended
BMI_cols = ['BIA-BIA_BMI', 'Physical-BMI', 'BMI_combined']
train_ds[BMI_cols]
[62]:
BIA-BIA_BMI Physical-BMI BMI_combined
0 16.8792 16.877316 16.878258
1 14.0371 14.035590 14.036345
2 NaN 16.648696 16.648696
3 18.2943 18.292347 18.293323
4 NaN NaN NaN
... ... ... ...
3955 16.3642 16.362460 16.363330
3956 NaN 18.764678 18.764678
3957 21.4438 21.441500 21.442650
3958 12.2372 12.235895 12.236548
3959 NaN NaN NaN

3960 rows × 3 columns

[63]:
#dropping the original BMI columns
train_ds = train_ds.drop(columns=['Physical-BMI', 'BIA-BIA_BMI'])
test_ds = test_ds.drop(columns=['Physical-BMI', 'BIA-BIA_BMI'])
##### Actigraphy data (train_ts)
Actigraphy data (train_ts)¶
Only 996 of the test subjects are in both train_ds and train_ts, meaning only this number of participants have both demographic data as well as actigraphy data.

Only 996 of the test subjects are in both train_ds and train_ts, meaning only this number of participants have both demographic data as well as actigraphy data.

[64]:
#merging train_ds and train_ts left on the ID values in train_ds, as there are more than those contained in train_ts
merged_train = pd.merge(train_ds, train_ts, how="left", on='id')
merged_train.head()

[64]:
id Basic_Demos-Enroll_Season Basic_Demos-Age Basic_Demos-Sex CGAS-Season CGAS-CGAS_Score Physical-Season Physical-Height Physical-Weight Physical-Waist_Circumference ... Stat_86 Stat_87 Stat_88 Stat_89 Stat_90 Stat_91 Stat_92 Stat_93 Stat_94 Stat_95
0 00008ff9 Fall 5 0 Winter 51.0 Fall 46.0 50.8 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 000fd460 Summer 9 0 NaN NaN Fall 48.0 46.0 22.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 00105258 Summer 10 1 Fall 71.0 Fall 56.5 75.6 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 00115b9f Winter 9 0 Fall 71.0 Summer 56.0 81.6 NaN ... 1.546979 4.004276 89.751656 0.0 2633.25 4188.5 8.611000e+13 7.0 3.0 85.0
4 0016bb22 Spring 18 1 Summer NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 177 columns

[65]:
# doing same for the test data
merged_test = pd.merge(test_ds, test_ts, how="left", on='id')
merged_test.head()
test_ids = merged_test['id'].to_list()
[66]:
merged_train = merged_train.drop(columns=['id'])
merged_test = merged_test.drop(columns=['id'])
##### Feature(s): PCIAT-PCIAT_[01 through 20], PCIAT-PCIAT_Total
Feature(s): PCIAT-PCIAT_[01 through 20], PCIAT-PCIAT_Total¶
In the training dataset, there are features for 20 different PCIAT () scores, as well as a feature for PCIAT Total and the season the PCIAT test was conducted. The PCIAT Total was used by the creators of this dataset to determine 'sii' score and therefore there are no PCIAT values in the test dataset. A model cannot be trained to depend on these features if it is going to then be used to generate predictions for the test data, so I will drop them now.

In the training dataset, there are features for 20 different PCIAT () scores, as well as a feature for PCIAT Total and the season the PCIAT test was conducted. The PCIAT Total was used by the creators of this dataset to determine 'sii' score and therefore there are no PCIAT values in the test dataset. A model cannot be trained to depend on these features if it is going to then be used to generate predictions for the test data, so I will drop them now.

[67]:
merged_train = merged_train.drop(columns=['PCIAT-PCIAT_01',
'PCIAT-PCIAT_02',
'PCIAT-PCIAT_03',
'PCIAT-PCIAT_04',
'PCIAT-PCIAT_05',
'PCIAT-PCIAT_06',
'PCIAT-PCIAT_07',
'PCIAT-PCIAT_08',
'PCIAT-PCIAT_09',
'PCIAT-PCIAT_10',
'PCIAT-PCIAT_11',
'PCIAT-PCIAT_12',
'PCIAT-PCIAT_13',
'PCIAT-PCIAT_14',
'PCIAT-PCIAT_15',
'PCIAT-PCIAT_16',
'PCIAT-PCIAT_17',
'PCIAT-PCIAT_18',
'PCIAT-PCIAT_19',
'PCIAT-PCIAT_20',
'PCIAT-PCIAT_Total',
'PCIAT-Season'
])


[68]:
merged_train.columns
[68]:
Index(['Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',
       'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-Height',
       'Physical-Weight', 'Physical-Waist_Circumference',
       'Physical-Diastolic_BP',
       ...
       'Stat_86', 'Stat_87', 'Stat_88', 'Stat_89', 'Stat_90', 'Stat_91',
       'Stat_92', 'Stat_93', 'Stat_94', 'Stat_95'],
      dtype='object', length=154)
[69]:
numeric_data = merged_train.select_dtypes(include=np.number)
numeric_data = numeric_data.iloc[:,:48]

numeric_data.head()
[69]:
Basic_Demos-Age Basic_Demos-Sex CGAS-CGAS_Score Physical-Height Physical-Weight Physical-Waist_Circumference Physical-Diastolic_BP Physical-HeartRate Physical-Systolic_BP Fitness_Endurance-Max_Stage ... BIA-BIA_LST BIA-BIA_SMM BIA-BIA_TBW PAQ_A-PAQ_A_Total PAQ_C-PAQ_C_Total SDS-SDS_Total_Raw SDS-SDS_Total_T PreInt_EduHx-computerinternet_hoursday sii BMI_combined
0 5 0 51.0 46.0 50.8 NaN NaN NaN NaN NaN ... 38.9177 19.5413 32.6909 NaN NaN NaN NaN 3.0 2.0 16.878258
1 9 0 NaN 48.0 46.0 22.0 75.0 70.0 122.0 NaN ... 39.4497 15.4107 27.0552 NaN 2.340 46.0 64.0 0.0 0.0 14.036345
2 10 1 71.0 56.5 75.6 NaN 65.0 94.0 117.0 5.0 ... NaN NaN NaN NaN 2.170 38.0 54.0 2.0 0.0 16.648696
3 9 0 71.0 56.0 81.6 NaN 60.0 97.0 117.0 6.0 ... 58.9338 26.4798 45.9966 NaN 2.451 31.0 45.0 0.0 1.0 18.293323
4 18 1 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN 1.04 NaN NaN NaN NaN NaN NaN

5 rows × 48 columns

[70]:
# numeric_data.corr()
# sns.set(rc={'figure.figsize':(30,30)})
# sns.heatmap(numeric_data.corr(), annot=True);
[71]:
df=numeric_data.corr().abs().stack().reset_index()
# .sort_values(0, ascending=False)
df['pairs'] = list(zip(df.level_0, df.level_1))

df.set_index(['pairs'], inplace = True)

df.drop(columns=['level_1', 'level_0'], inplace = True)

df.columns = ['cc']

df.drop_duplicates(inplace=True)
[72]:
correlations = numeric_data.corr()['sii'] > 0.20
correlations
[72]:
Basic_Demos-Age                            True
Basic_Demos-Sex                           False
CGAS-CGAS_Score                           False
Physical-Height                            True
Physical-Weight                            True
Physical-Waist_Circumference               True
Physical-Diastolic_BP                     False
Physical-HeartRate                        False
Physical-Systolic_BP                      False
Fitness_Endurance-Max_Stage               False
Fitness_Endurance-Time_Mins               False
Fitness_Endurance-Time_Sec                False
FGC-FGC_CU                                 True
FGC-FGC_CU_Zone                           False
FGC-FGC_GSND                              False
FGC-FGC_GSND_Zone                         False
FGC-FGC_GSD                               False
FGC-FGC_GSD_Zone                          False
FGC-FGC_PU                                False
FGC-FGC_PU_Zone                           False
FGC-FGC_SRL                               False
FGC-FGC_SRL_Zone                          False
FGC-FGC_SRR                               False
FGC-FGC_SRR_Zone                          False
FGC-FGC_TL                                False
FGC-FGC_TL_Zone                           False
BIA-BIA_Activity_Level_num                False
BIA-BIA_BMC                               False
BIA-BIA_BMR                               False
BIA-BIA_DEE                               False
BIA-BIA_ECW                               False
BIA-BIA_FFM                               False
BIA-BIA_FFMI                              False
BIA-BIA_FMI                               False
BIA-BIA_Fat                               False
BIA-BIA_Frame_num                         False
BIA-BIA_ICW                               False
BIA-BIA_LDM                               False
BIA-BIA_LST                               False
BIA-BIA_SMM                               False
BIA-BIA_TBW                               False
PAQ_A-PAQ_A_Total                         False
PAQ_C-PAQ_C_Total                         False
SDS-SDS_Total_Raw                          True
SDS-SDS_Total_T                            True
PreInt_EduHx-computerinternet_hoursday     True
sii                                        True
BMI_combined                               True
Name: sii, dtype: bool
[73]:
Selection deleted
top_corr = df[(df.cc>.80) & (df.cc <1)]
[74]:
# top_corr.corr()
sns.set(rc={'figure.figsize':(30,30)})
sns.heatmap(top_corr, annot=True);
##### Feature(s): PreInt_EduHx-Season, SDS-Season, PAQ_C-Season, PAQ_A-Season, BIA-Season, FGC-Season, Fitness_Endurance-Season, Physical-Season, CGAS-Season, Basic_Demos-Enroll_Season, PCIAT-Season
Feature(s): PreInt_EduHx-Season, SDS-Season, PAQ_C-Season, PAQ_A-Season, BIA-Season, FGC-Season, Fitness_Endurance-Season, Physical-Season, CGAS-Season, Basic_Demos-Enroll_Season, PCIAT-Season¶
There are multiple features in the data that refer to **seasons**. To make these feature values interpretable in modeling, I label encode them so that each season name will be replaced by a corresponding integer.

There are multiple features in the data that refer to seasons. To make these feature values interpretable in modeling, I label encode them so that each season name will be replaced by a corresponding integer.

[75]:
#label encoding all features that are seasons

from sklearn import preprocessing

label_encoder = preprocessing.LabelEncoder()
merged_train['PreInt_EduHx-Season']= label_encoder.fit_transform(merged_train['PreInt_EduHx-Season'])
merged_train['SDS-Season']= label_encoder.fit_transform(merged_train['SDS-Season'])
merged_train['PAQ_C-Season']= label_encoder.fit_transform(merged_train['PAQ_C-Season'])
merged_train['PAQ_A-Season']= label_encoder.fit_transform(merged_train['PAQ_A-Season'])
merged_train['BIA-Season']= label_encoder.fit_transform(merged_train['BIA-Season'])
merged_train['FGC-Season']= label_encoder.fit_transform(merged_train['FGC-Season'])
merged_train['Fitness_Endurance-Season']= label_encoder.fit_transform(merged_train['Fitness_Endurance-Season'])
merged_train['Physical-Season']= label_encoder.fit_transform(merged_train['Physical-Season'])
merged_train['CGAS-Season']= label_encoder.fit_transform(merged_train['CGAS-Season'])
merged_train['Basic_Demos-Enroll_Season']= label_encoder.fit_transform(merged_train['Basic_Demos-Enroll_Season'])

#To view the results of this encoding on one of these features:
merged_train['PreInt_EduHx-Season'].unique()



[75]:
array([0, 2, 3, 4, 1])
[76]:
# doing same for test set
merged_test['PreInt_EduHx-Season']= label_encoder.fit_transform(merged_test['PreInt_EduHx-Season'])
merged_test['SDS-Season']= label_encoder.fit_transform(merged_test['SDS-Season'])
merged_test['PAQ_C-Season']= label_encoder.fit_transform(merged_test['PAQ_C-Season'])
merged_test['PAQ_A-Season']= label_encoder.fit_transform(merged_test['PAQ_A-Season'])
merged_test['BIA-Season']= label_encoder.fit_transform(merged_test['BIA-Season'])
merged_test['FGC-Season']= label_encoder.fit_transform(merged_test['FGC-Season'])
merged_test['Fitness_Endurance-Season']= label_encoder.fit_transform(merged_test['Fitness_Endurance-Season'])
merged_test['Physical-Season']= label_encoder.fit_transform(merged_test['Physical-Season'])
merged_test['CGAS-Season']= label_encoder.fit_transform(merged_test['CGAS-Season'])
merged_test['Basic_Demos-Enroll_Season']= label_encoder.fit_transform(merged_test['Basic_Demos-Enroll_Season'])

##### Feature(s): sii (target)
Feature(s): sii (target)¶
[77]:
merged_train['sii'].isnull().sum()

[77]:
1224
There are a lot of rows in the data that have null values as the target. Therefore, without preprocessing further, fully supervised modeling techiniques would not be possible on this data.

There are a lot of rows in the data that have null values as the target. Therefore, without preprocessing further, fully supervised modeling techiniques would not be possible on this data.

[78]:
#remove target for now, while I impute other missing values
target_col = merged_train.pop('sii')
[79]:
from sklearn.impute import KNNImputer
knn_imputer = KNNImputer(missing_values=np.nan, n_neighbors=8)
filled_array = knn_imputer.fit_transform(merged_train)
filled_train = pd.DataFrame(filled_array, columns = merged_train.columns)
[80]:
# #doing same to test set
# filled_test_array = knn_imputer.fit_transform(merged_test)
# filled_test = pd.DataFrame(filled_test_array, columns = merged_test.columns)
[81]:
#doing same to test set
filled_test_array = knn_imputer.transform(merged_test)
filled_test = pd.DataFrame(filled_test_array, columns = merged_test.columns)
## Modeling

Modeling¶

With sklearn's SelfTrainingClassifier, you can use a regular classifier in a semi-supervised way. I am going to use the best performing model from the supervised modeling techniques to do this.

With sklearn's SelfTrainingClassifier, you can use a regular classifier in a semi-supervised way. I am going to use the best performing model from the supervised modeling techniques to do this.

[82]:
#adding back in the target column
filled_train.insert(len(filled_train.columns), 'sii', target_col)

# filling missing target values with -1 for semi-supervised model
filled_train = filled_train.fillna(value=-1)
[ ]:
X_train_ss=filled_train.drop(columns=['sii'])
y_train_ss=filled_train['sii'].values
X_test_ss=filled_train.drop(columns=['sii'])
y_test_ss=filled_train['sii'].values
[ ]:
scaler = StandardScaler()
scaled_X_train = scaler.fit_transform(X_train_ss)
scaled_X_test = scaler.transform(X_test_ss)


[83]:

base_classifier = XGBClassifier()


self_training_model = SelfTrainingClassifier(base_estimator=base_classifier, # An estimator object implementing fit and predict_proba.


threshold=0.7, # default=0.75, The decision threshold for use with criterion='threshold'. Should be in [0, 1).
criterion='k_best', # {‘threshold’, ‘k_best’}, default=’threshold’, The selection criterion used to select which labels to add to the training set. If 'threshold', pseudo-labels with prediction probabilities above threshold are added to the dataset. If 'k_best', the k_best pseudo-labels with highest prediction probabilities are added to the dataset.
k_best=50, # default=10, The amount of samples to add in each iteration. Only used when criterion='k_best'.
max_iter=100, # default=10, Maximum number of iterations allowed. Should be greater than or equal to 0. If it is None, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled.
verbose=True # default=False, Verbosity prints some information after each iteration
)
[84]:
st = self_training_model.fit(X_train_ss, y_train_ss)
preds_st = st.predict(X_test_ss)
End of iteration 1, added 50 new labels.
End of iteration 2, added 50 new labels.
End of iteration 3, added 50 new labels.
End of iteration 4, added 50 new labels.
End of iteration 5, added 50 new labels.
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End of iteration 8, added 50 new labels.
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End of iteration 10, added 50 new labels.
End of iteration 11, added 50 new labels.
End of iteration 12, added 50 new labels.
End of iteration 13, added 50 new labels.
End of iteration 14, added 50 new labels.
End of iteration 15, added 50 new labels.
End of iteration 16, added 50 new labels.
End of iteration 17, added 50 new labels.
End of iteration 18, added 50 new labels.
End of iteration 19, added 50 new labels.
End of iteration 20, added 50 new labels.
End of iteration 21, added 50 new labels.
End of iteration 22, added 50 new labels.
End of iteration 23, added 50 new labels.
End of iteration 24, added 50 new labels.
End of iteration 25, added 24 new labels.
[85]:
cohen_kappa_score(y_test_ss, preds_st, weights='quadratic')
[85]:
0.7725622721296541
[ ]:
# X = filled_train.drop(columns=['sii'])
# y = filled_train[['sii']]
[ ]:
# scaler = StandardScaler()
# scaled_X = scaler.fit_transform(X)

[ ]:
# #Train-test-splitting the data for modeling



# X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size = 0.25, random_state = 42)


[ ]:
# #formatting the target values

# y_train = y_train.values.ravel()
# y_test = y_test.values.ravel()
[ ]:

# rf = RandomForestClassifier()
# # Specify Self-Training model parameters
# self_training_model_baseline = SelfTrainingClassifier(base_estimator=rf, # An estimator object implementing fit and predict_proba.
# threshold=0.7, # default=0.75, The decision threshold for use with criterion='threshold'. Should be in [0, 1).
# criterion='threshold', # {‘threshold’, ‘k_best’}, default=’threshold’, The selection criterion used to select which labels to add to the training set. If 'threshold', pseudo-labels with prediction probabilities above threshold are added to the dataset. If 'k_best', the k_best pseudo-labels with highest prediction probabilities are added to the dataset.
# k_best=50, # default=10, The amount of samples to add in each iteration. Only used when criterion='k_best'.
# max_iter=100, # default=10, Maximum number of iterations allowed. Should be greater than or equal to 0. If it is None, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled.
# verbose=True # default=False, Verbosity prints some information after each iteration
# )


# st_b = self_training_model_baseline.fit(X_train, y_train)
[ ]:
# preds_st_b = st_b.predict(X_test)
# cohen_kappa_score(y_test, preds_st_b, weights='quadratic')
[ ]:

# abc = AdaBoostClassifier()
# # Specify Self-Training model parameters
# self_training_model_1 = SelfTrainingClassifier(base_estimator=abc, # An estimator object implementing fit and predict_proba.
# threshold=0.7, # default=0.75, The decision threshold for use with criterion='threshold'. Should be in [0, 1).
# criterion='threshold', # {‘threshold’, ‘k_best’}, default=’threshold’, The selection criterion used to select which labels to add to the training set. If 'threshold', pseudo-labels with prediction probabilities above threshold are added to the dataset. If 'k_best', the k_best pseudo-labels with highest prediction probabilities are added to the dataset.
# k_best=50, # default=10, The amount of samples to add in each iteration. Only used when criterion='k_best'.
# max_iter=100, # default=10, Maximum number of iterations allowed. Should be greater than or equal to 0. If it is None, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled.
# verbose=True # default=False, Verbosity prints some information after each iteration
# )


# st_1 = self_training_model_1.fit(X_train, y_train)
[ ]:
# preds_st1 = st_1.predict(X_test)
# cohen_kappa_score(y_test, preds_st1, weights='quadratic')
[ ]:

# xgb = XGBClassifier()
# # Specify Self-Training model parameters
# self_training_model2 = SelfTrainingClassifier(base_estimator=xgb, # An estimator object implementing fit and predict_proba.
# threshold=0.7, # default=0.75, The decision threshold for use with criterion='threshold'. Should be in [0, 1).
# criterion='threshold', # {‘threshold’, ‘k_best’}, default=’threshold’, The selection criterion used to select which labels to add to the training set. If 'threshold', pseudo-labels with prediction probabilities above threshold are added to the dataset. If 'k_best', the k_best pseudo-labels with highest prediction probabilities are added to the dataset.
# k_best=50, # default=10, The amount of samples to add in each iteration. Only used when criterion='k_best'.
# max_iter=100, # default=10, Maximum number of iterations allowed. Should be greater than or equal to 0. If it is None, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled.
# verbose=True # default=False, Verbosity prints some information after each iteration
# )


# st2 = self_training_model2.fit(X_train, y_train)
[ ]:
# preds_st2 = st2.predict(X_test)
# cohen_kappa_score(y_test, preds_st2, weights='quadratic')
Of the different model types used with SelfTrainingClassifier in a semi-supervised way, it appears that the XGBoostingClassifier model performs the best.

Of the different model types used with SelfTrainingClassifier in a semi-supervised way, it appears that the XGBoostingClassifier model performs the best.

#### Hyperparameter Tuning

Hyperparameter Tuning¶

#### Hyperparameter Tuning with GridSearchCV

Hyperparameter Tuning with GridSearchCV¶

The best performing model so far, in terms of quadratic weighted kappa score, was the __ model. By tuning the hyperparameters of this model, can this metric be improved even further?

The best performing model so far, in terms of quadratic weighted kappa score, was the __ model. By tuning the hyperparameters of this model, can this metric be improved even further?

[ ]:
# # Create a scorer for quadratic weighted kappa
# qwk_scorer = make_scorer(cohen_kappa_score, weights='quadratic')

# # Define the self-training model
# model = SelfTrainingClassifier(base_estimator=xgb)

# # Define hyperparameter distribution
# param_dist = {
# 'base_estimator__max_depth': [3, 5, 10, None],
# # 'threshold': [0.7, 0.5, 0.3],
# 'criterion': ['k_best', 'threshold'],
# # 'k_best':[10, 25, 50],
# # 'max_iter'=[10,50,100]
# }

# # scoring = cohen_kappa_score(X_test, y_test, weights='quadratic')
# # Perform random search
# gridsearch = GridSearchCV(estimator=model,
# param_grid=param_dist,
# # n_iter=100,
# cv=5,
# # scoring=qwk_scorer,
# )
# gridsearch.fit(X_train, y_train)

# # Report best score and parameters
# print(f"Best score: {gridsearch.best_score_:.3f}")
# print(f"Best parameters: {gridsearch.best_params_}")

# # Evaluate on test set
# best_model = gridsearch.best_estimator_
# qwk = best_model.score(X_test, y_test)
# print(f"Test set Quadratic Weighted Kappa Score: {qwk:.3f}")

[ ]:
# tuned_preds = best_model.predict(X_test)
[ ]:
# cohen_kappa_score(y_test, tuned_preds, weights='quadratic')
[ ]:
# from sklearn.metrics import confusion_matrix
# cnf_matrix = confusion_matrix(y_test, tuned_preds)
# # np.set_printoptions(precision=2)
### Generating predictions for submission

Generating predictions for submission¶

[ ]:
# #standardize
# filled_test

# scaler = StandardScaler()
# scaled_X = scaler.fit_transform(filled_test)
[ ]:
final_preds = st.predict(filled_test)
[ ]:
submission = pd.DataFrame({'id': test_ids,'sii': final_preds})

# Save the submission to a CSV file
submission.to_csv('submission.csv', index=False)

# Check the first few rows of the submission
print(submission.head())
## Results & Conclusion

Results & Conclusion¶

#### Results:
After hyperparameter tuning, the best model was achieved with a quadratic weighted kappa score of _.

Results:¶

After hyperparameter tuning, the best model was achieved with a quadratic weighted kappa score of _.

#### Limitations:
- The data provided for this task is very sparse, containing a large amount of null values for most features, including the target (sii). This means that in order to accomplish this project task, either an unsupervised learning model needs to be used, which can often be difficult to evaluate, or these values need to be dealt with prior to modeling. I chose a semi-supervised modeling approach that would use existing target values to first predict and fill the missing values before being used to generate test set predictions. However effective this method may be, we don't know for sure if these predicted target values are correct, or if the imputed missing values for the other features are accurate. When this filled-in data is used for modeling, the model may detect patterns that are not really there or not as strong as they are in reality. This can have unseen impacts on the results of modeling.
- Similarly to the previous point, some of the features present in the training dataset (train_ds), are not present in the test dataset (test_ds). This means that if the model relies on these features during training to make predictions, then the model will not be able to make predictions as well for the test dataset.
- Another limitation of the data is the very large number of features. This increases the complexity of the data, which can affect the modeling process. More complex data can lead to model overfitting and thus the model will not perform as well on unseen data. I used multiple techniques to reduce the impact of this, however there may be other techniques that would work even better and reduce this complexity even further.

Limitations:¶

  • The data provided for this task is very sparse, containing a large amount of null values for most features, including the target (sii). This means that in order to accomplish this project task, either an unsupervised learning model needs to be used, which can often be difficult to evaluate, or these values need to be dealt with prior to modeling. I chose a semi-supervised modeling approach that would use existing target values to first predict and fill the missing values before being used to generate test set predictions. However effective this method may be, we don't know for sure if these predicted target values are correct, or if the imputed missing values for the other features are accurate. When this filled-in data is used for modeling, the model may detect patterns that are not really there or not as strong as they are in reality. This can have unseen impacts on the results of modeling.
  • Similarly to the previous point, some of the features present in the training dataset (train_ds), are not present in the test dataset (test_ds). This means that if the model relies on these features during training to make predictions, then the model will not be able to make predictions as well for the test dataset.
  • Another limitation of the data is the very large number of features. This increases the complexity of the data, which can affect the modeling process. More complex data can lead to model overfitting and thus the model will not perform as well on unseen data. I used multiple techniques to reduce the impact of this, however there may be other techniques that would work even better and reduce this complexity even further.
#### Next Steps:
Although the final model performed very well, it is possible that this result could be improved even further by trying out different techniques in pre-processing and modeling. In future research, this result may be improve by:
- including data from a greater number of test subjects
- including more complete data for test subjects
- experimenting with other ways of imputing/filling missing values including target values (sii)
- experimenting with other types of models for multiclass classification

Next Steps:¶

Although the final model performed very well, it is possible that this result could be improved even further by trying out different techniques in pre-processing and modeling. In future research, this result may be improve by:

  • including data from a greater number of test subjects
  • including more complete data for test subjects
  • experimenting with other ways of imputing/filling missing values including target values (sii)
  • experimenting with other types of models for multiclass classification
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  • Clear Outputs of All Cells
    Clear all outputs of all cells
  • Close and Shut Down Notebook
  • Collapse All Headings
    ⌃ ⇧ ←
  • Deselect All Cells
  • Enter Command Mode
    ⌃ M
  • Enter Edit Mode
    ⏎
  • Expand All Headings
    ⌃ ⇧ →
  • Interrupt Kernel
    Interrupt the kernel
  • New Console for Notebook
  • New Notebook
    Create a new notebook
  • Reconnect to Kernel
  • Render All Markdown Cells
  • Restart Kernel and Clear Outputs of All Cells…
    Restart the kernel and clear all outputs of all cells
  • Restart Kernel and Debug…
    Restart Kernel and Debug…
  • Restart Kernel and Run All Cells…
    Restart the kernel and run all cells
  • Restart Kernel and Run up to Selected Cell…
  • Restart Kernel…
    Restart the kernel
  • Run All Above Selected Cell
  • Run All Cells
    Run all cells
  • Run Selected Cell and All Below
  • Select All Cells
    ⌘ A
  • Show Line Numbers
    ⇧ L
  • Toggle Collapse Notebook Heading
  • Trust Notebook
  • Other
  • Open in JupyterLab
    JupyterLab
  • Open in NbClassic
    NbClassic
  • Terminal
  • Decrease Terminal Font Size
  • Increase Terminal Font Size
  • New Terminal
    Start a new terminal session
  • Refresh Terminal
    Refresh the current terminal session
  • Use Terminal Theme: Dark
    Set the terminal theme
  • Use Terminal Theme: Inherit
    Set the terminal theme
  • Use Terminal Theme: Light
    Set the terminal theme
  • Text Editor
  • Decrease Font Size
  • Increase Font Size
  • New Markdown File
    Create a new markdown file
  • New Python File
    Create a new Python file
  • New R File
    Create a new R file
  • New Text File
    Create a new text file
  • Spaces: 1
  • Spaces: 2
  • Spaces: 4
  • Spaces: 4
  • Spaces: 8
  • Theme
  • Decrease Code Font Size
  • Decrease Content Font Size
  • Decrease UI Font Size
  • Increase Code Font Size
  • Increase Content Font Size
  • Increase UI Font Size
  • Theme Scrollbars
  • Use Theme: JupyterLab Dark
  • Use Theme: JupyterLab Light
  • View
  • File Browser
  • Open JupyterLab
  • Show Header
  • Show Notebook Tools
    Show Show Notebook Tools in the right sidebar
  • Show Table of Contents
    Show Show Table of Contents in the left sidebar